Download Regional Cerebral Arterial Transit Time Hemodynamics Correlate

Published August 21, 2014 as 10.3174/ajnr.A4094
ORIGINAL RESEARCH
BRAIN
Regional Cerebral Arterial Transit Time Hemodynamics
Correlate with Vascular Risk Factors and Cognitive Function in
Men with Coronary Artery Disease
B.J. MacIntosh, W. Swardfager, A.D. Robertson, E. Tchistiakova, M. Saleem, P.I. Oh, N. Herrmann, B. Stefanovic, and K.L. Lanctôt
ABSTRACT
BACKGROUND AND PURPOSE: Arterial transit time is the time needed for blood to travel from large arteries to capillaries, as estimated
from arterial spin-labeling MR imaging. The purpose of this study was to determine whether vascular risk factors and cognitive performance are related to regional differences in cerebral arterial transit time in patients with coronary artery disease who are at risk for
cognitive decline.
MATERIALS AND METHODS: Arterial transit time was estimated from multiple postlabel delay pseudocontinuous arterial spin-labeling
images obtained from 29 men with coronary artery disease. Tests of memory, attention, processing speed, and executive function were
administered. Principal component analysis was used to create separate models of cognition and vascular risk, which were related to brain
regions through voxelwise analyses of arterial transit time maps.
RESULTS: Principal component analysis identified 2 components of vascular risk: 1) “pressor” (age, systolic blood pressure, and pulse
pressure) and 2) “obesity” (body fat percentage and body mass index). Obesity was inversely related to arterial transit time in the posterior
cingulate, precuneus, lateral occipital cortices, middle temporal gyrus, and frontal pole (P corrected ⬍ .05), whereas pressor was not
significant. Cognitive scores were factored into a single component. Poor performance was inversely related to precuneus arterial transit
time (P corrected ⬍ .05). The average arterial transit time in regions identified by obesity was associated with poorer cognitive function
(r2 ⫽ 0.21, t ⫽ ⫺2.65, P ⫽ .01).
CONCLUSIONS: Altered cerebral hemodynamics, notably in nodal structures of the default mode network, may be one way that vascular
risk factors impact cognition in patients with coronary artery disease.
ABBREVIATIONS: ASL ⫽ arterial spin-labeling; ATT ⫽ arterial transit time; BMI ⫽ body mass index; CAD ⫽ coronary artery disease; cog-PC ⫽ cognitive principal
component; CVLT-LDFR ⫽ California Verbal Learning Test long-delay free recall; LATT ⫽ large-artery transit time; PC ⫽ principal component; SBP ⫽ systolic blood
pressure
O
besity and hypertension are prevalent vascular risk factors
among older adults, known to impact brain structure and
function. Elevated body mass index (BMI), for example, has been
associated with reduced gray matter volume.1,2 Furthermore,
Received May 28, 2014; accepted July 8.
From the Canadian Partnership for Stroke Recovery (B.J.M., W.S., A.D.R., E.T., N.H.,
B.S., K.L.L.), Physical Sciences (B.J.M., B.S.), and Neuropsychopharmacology Research
Group (W.S., M.S., N.H., K.L.L.), Sunnybrook Research Institute, Toronto, Ontario,
Canada; Departments of Medical Biophysics (B.J.M., E.T., B.S.,), Pharmacology/Toxicology (K.L.L., W.S.), and Psychiatry (N.H., K.L.L.), University of Toronto, Toronto,
Ontario, Canada; and Toronto Rehabilitation Institute (P.I.O., K.L.L.), Toronto, Ontario, Canada.
This work was funded by The Drummond Foundation (K.L.L., N.H.), Physicians’ Services Incorporated Foundation (K.L.L., N.H.), and the Canadian Institutes of Health
Research (K.L.L., N.H.: MOP-114913). W.S. was supported by the Canadian Partnership for Stroke Recovery, the Canadian Institutes of Health and Research and by
the Toronto Rehabilitation Institute.
Please address correspondence to Bradley J. MacIntosh, PhD, Canadian Partnership
for Stroke Recovery, Sunnybrook Research Institute, 075 Bayview Ave, M6-180,
Toronto, ON, M4N 3M5, Canada; e-mail: [email protected]; @HSFCSR
overweight or obese classifications in midlife have been linked to
decline in cognitive performance, involving memory, processing
speed, verbal fluency, and visuospatial domains3 and an increased
risk of Alzheimer disease and vascular dementia.4 Likewise, elevated systolic blood pressure (SBP) has been associated with reduced cerebral tissue, which appears to be more specific to men
than women.5 Elevated SBP also has been associated with cognitive impairment, namely executive function, among older adults.6
Taking these findings into account, researchers have suggested
that vascular risk factors contribute to cognitive impairment
through cerebrovascular dysfunction itself.7,8
In animal studies, hypertension and obesity lead to cerebrovascular remodeling, including thickened arteriolar walls, reduced lumen area, and rarefaction of the microcirculation.9 ArIndicates open access to non-subscribers at www.ajnr.org
Indicates article with supplemental on-line photo.
http://dx.doi.org/10.3174/ajnr.A4094
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terial spin-labeling (ASL) is typically used to measure CBF as a
single measure of perfusion; acquiring multiple postlabel delays
permits more comprehensive hemodynamic estimates, including
arterial transit time (ATT). Although ASL is a low SNR technique,
considerable research has gone into modeling approaches that incorporate spatial and temporal information to produce robust voxellevel estimates.10-13 Mapping the ASL arterial transit time, for instance, is of interest because it reflects the time for labeled blood to
travel from the labeling plane to the microvascular perfusion site.
Previously, ATT has been used as an adjunct perfusion measure to
study minor stroke and transient ischemic attack,14 large artery steno-occlusive disease,15,16 multiple sclerosis,17 and Alzheimer disease,18,19 but to date, it has not been used to examine the potential
effects of vascular risk factors on cognition, to our knowledge.
We have recently reported gray matter perfusion findings in
coronary artery disease (CAD) in the context of cerebrovascular
health.20 This population is relevant to the study of neurodegenerative disease risk because of the following: 1) They present with
multiple vascular risk factors that impact both the heart and the
brain, 2) have a propensity for small-vessel disease affecting the white
matter, and 3) have a higher susceptibility to cognitive decline.21,22 In
older adults, obesity was recently reported to affect the functional
connectivity of the default mode network.23 We hypothesized that
regional ATT would be associated with aggregate measures of both
vascular risk and cognitive function in men with CAD.
MATERIALS AND METHODS
Participants
Twenty-nine male patients with a history of CAD provided written informed consent and were recruited for this study. Sunnybrook Research Institute and University Health Network research
ethics boards approved this study. After providing a detailed clinical history, patients underwent MR imaging and performed a
battery of cognitive tests. Inclusion criteria were male sex, 55– 80
years of age, and a documented history of CAD characterized by
myocardial infarction, narrowing of at least 1 major coronary
artery on angiography, prior percutaneous coronary intervention, or coronary artery bypass graft surgery. This study excluded
women due to the low referral rate of women to the cardiac rehabilitation program24 and reported differences in ATT between
men and women,25 which would have necessitated a larger sample
to include sex as a covariate. Patients were excluded on the basis of
a history of any neurodegenerative disorder. Demographic information, concomitant medications, anthropometrics, resting
blood pressure, and history of hyperlipidemia, diabetes mellitus,
hypertension, and smoking were ascertained, in addition to cardiac history.
MR Imaging
MR imaging was performed on a 3T system (Discovery MR750;
GE Healthcare, Milwaukee, Wisconsin) by using a radiofrequency
body coil for transmission and an 8-channel phased array
radiofrequency head coil for signal detection. High-resolution
T1-w images were collected by using 3D spoiled gradient-recalled
echo (TR/TE/TI ⫽ 8.1/3.2/650 ms, flip angle ⫽ 8°, acquisition
matrix ⫽ 256 ⫻ 192 ⫻ 186, nominal spatial resolution ⫽ 0.9 ⫻
0.9 ⫻ 1 mm). Standard T2-weighted FLAIR images were acquired
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to characterize white matter hyperintensities (2D axial images,
TR/TE/TI ⫽ 9700/140/2200 ms, voxel dimensions ⫽ 0.9 ⫻ 0.9 ⫻
3 mm, sections ⫽ 48). A semiautomated procedure was used to
quantify the volume of white matter lesion burden (milliliter).26
Pseudocontinuous ASL was performed with a label duration of
1.5 seconds, and the labeling plane was prescribed above the carotid bifurcation, aided by time-of-flight angiography images.
Axial single-shot EPI was used to collect 25 sequential control and
tag images. Seventeen slices were collected with a gap of 1.4 mm,
section thickness of 4.2 mm, and nominal voxel dimensions of
3.4 ⫻ 3.4 ⫻ 5.6 mm3. The acquisition was repeated at 6 different
postlabel delays (ie, 100, 500, 900, 1300, 1700, and 2100 ms) to
produce an ASL kinetic time-series (On-line Figure), as reported
previously.27 In keeping with the recommended guidelines for
clinical ASL, we did not use bipolar gradients to suppress largeartery flow signal.28 Instead macrovascular signals were isolated,
when appropriate, in the ASL model (see below).
Postprocessing
ASL images were processed by using the fMRI of the Brain Software Library tools (FSL; http://www.fmrib.ox.ac.uk/fsl). Postprocessing of ASL data included perfusion-weighted difference images, motion correction, and spatial smoothing by a Gaussian
kernel of 5-mm full width at half maximum. Hemodynamic parameter estimates were generated by using the Bayesian Inference
for Arterial Spin Labeling tool of FSL. ATT images were coregistered to the T1-w images by using transformation matrices established by CBF images, because both T1-w and CBF images contain
good gray-to-white differentiation for registration purposes.
T1-w images was subsequently aligned to a standard space template with 12 degrees of freedom.
ATT (seconds) was the primary cerebral gray matter hemodynamic outcome measure of interest in this study. ATT was estimated on the basis of the standard ASL model29 and by using a
fixed-label duration of 1.5 seconds. A 1-compartment model (tissue only) and a 2-compartment model (tissue and macrovascular
compartments) were considered. The former produces ATT and
CBF estimates, while the latter produces ATT, CBF, large artery
transit time (LATT, seconds), and arterial blood volume estimates.12 Most important, the 2-compartment model takes effect
only in cases in which the voxel signal is deemed to be mixed
between tissue and macrovascular compartments. This procedure
is performed by using an automatic relevancy determination,
whereby LATT and arterial blood volume macrovascular estimates are generated in addition to ATT and CBF.12 The macrovascular estimates typically occur in voxels near large intracranial
arteries, like the circle of Willis (LATT and arterial blood volume
maps are shown in Fig 1). CBF and LATT data were included in
secondary analyses.
Neuropsychological Testing
Cognitive assessments were chosen on the basis of the recommendations of the National Institute of Neurological Disorders and
Stroke and Canadian Stroke Network harmonized standards.30
The Trail-Making Test B (connecting a sequence of alternating
letters and numbers) and the Victoria version of the Stroop interference task (color-word) were chosen for their sensitivity to ex-
FIG 1. The top row of images shows cerebral blood flow (intensity-normalized units), arterial transit time, large-artery transit time, and arterial
blood volume (arbitrary units) from a representative participant. In the ATT image, the posterior circulation tends to have a longer ATT
compared with the middle cerebral artery vascular territory. LATT and arterial blood volume are macrovascular measures that are estimated
from voxels containing large-artery/intravascular ASL signal. The bottom row shows 4 lobe-based regions of interest chosen for ATT (and CBF)
analysis, while a middle cerebral artery/insula region of interest was chosen the LATT analysis.
ecutive function (eg, set shifting and overcoming interference between conflicting visual and lexical cognitive sets). These are
timed tasks, with greater time to completion indicating poorer
performance. The digit symbol-coding task from the Wechsler
Adult Intelligence Scale, 3rd Edition, was chosen as a highly sensitive measure of complex attention and psychomotor processing
speed. Digit symbol-coding performance is summarized by the
number of symbols matched to digits based on a provided key
within 2 minutes. Memory was assessed by the California Verbal
Learning Test (CVLT) word list after a 20-minute delay (longdelay free recall [LDFR]). For the latter 2 tasks, greater scores
indicate better performance. All cognitive testing was performed
at 9:30 AM (⫾30 minutes) after an 8-hour fast.
Statistics
Analyses of the 2-compartment ATT data were performed in standard space at 3-mm isotropic voxel dimensions (Montreal Neurological Institute standard atlas). ATT estimates from the 1-compartment model were also analyzed to assess the effect of the ASL
model on the results. ATT was characterized by using the 4 bilateral lobes of the standard atlas brain (3-mm isotropic) as gray
matter ROIs (frontal, occipital, parietal, temporal; Fig 1) and by
using voxelwise approaches. LATT was analyzed from a single
region of interest in the bilateral insula.12 Two separate models
were constructed to explain between-subject variance in ATT,
CBF, and LATT data: 1) a vascular risk factor model, and 2) a
cognitive model. The vascular risk model consisted of the following variables: age, BMI, percentage body fat, SBP, and pulse pressure (ie, the difference between systolic and diastolic pressures).
The cognitive model consisted of age and raw scores on the
CVLT-LDFR, digit symbol-coding test, Stroop, and Trail-Making
Test B. Principal component analysis was performed in R statistical computing software (http://www.r-project.org/) to extract
uncorrelated vascular risk and cognitive predictors of ATT. Principal components (PCs) were considered in the model if individually they accounted for at least 10% of the total variance and
collectively, ⬎80% of the total explained variance.
General linear models were conducted by using R for the region-of-interest analyses and FSL for voxelwise group analyses.
Explanatory variables in the general linear models were the demeaned vascular risk or cognitive PCs, and the 2 predictors were
treated separately. For the region-of-interest analyses, a Bonferroni correction was performed to compute adjusted P values. For
the voxelwise analyses, statistical maps were calculated by using
permutation testing (ie, the FSL Randomise tool).31 Five thousand permutations were performed followed by threshold-free
cluster enhancement32 to correct for multiple comparisons.
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Significant voxels were reported at a corrected P value of .05
(pcorrected ⫽ .05).
RESULTS
␤-blockers (69.0%), antihypertensive agents (55.2%), and
antidiabetic agents (13.8%). FLAIR white matter hyperintensities amounted to a median lesion burden of 2.63 mL (interquartile range ⫽ 1.54 – 6.76 mL).
Patient Characteristics
Patient demographics and characteristics are reported in Table 1.
Evidence of severe symptomatic CAD was demonstrated by histories of myocardial infarction (41.4%), coronary artery bypass
graft surgery (48.3%), and percutaneous coronary intervention
(44.8%). All patients had a history of hyperlipidemia. Histories
of diabetes mellitus (13.8%), hypertension (41.4%), and
smoking (55.2%) were also common. All patients were using
acetylsalicylic acid. The most common concomitant medications included cholesterol-lowering medication (96.6%),
Table 1: Patient demographics (N ⴝ 29)
Demographic
Mean (SD)
Minimum
Age (yr)
65 (7)
55
28.2 (3.9)
20.5
BMI (kg/m2)
Body fat (%)
26.0 (5.9)
13.3
SBP (mm Hg)
124 (15.4)
94.0
PP (mm Hg)
51 (13.0)
24.0
Maximum
78
35.3
38.7
152.0
80.0
Note:—PP indicates pulse pressure.
Table 2: Vascular risk and cognitive model details
Correlation Coefficient (r)
Component 1 Component 2 Component 3
Vascular model
Age (yr)
BMI (kg/m2)
Body fat (%)
SBP (mm Hg)
PP (mm Hg)
Percentage of variance (%)
Explained variance
Cumulative variance
Cognitive model
Age (yr)
CVLT-LDFR
Digit symbol-coding
Stroop
TMT B
Percentage of variance (%)
Explained variance
⫺0.65
0.23
0.24
⫺0.90
⫺0.94
0.32
0.91
0.91
0.18
0.06
0.69
⫺0.06
⫺0.10
⫺0.32
⫺0.21
0.45
0.45
0.36
0.81
0.13
0.94
0.48
⫺0.73
⫺0.70
0.85
0.82
0.76
⫺0.37
0.53
⫺0.06
⫺0.26
⫺0.39
⫺0.50
0.30
⫺0.16
0.21
Vascular Risk Factors versus Regional Hemodynamics
The first 3 PCs explained ⬎80% of the vascular risk data and
were subsequently used in this model. PC1, PC2, and PC3
explained 45%, 36%, and 13% of the variance, respectively
(Table 2). PC1 was viewed as a “pressor” variable because SBP
and pulse pressure contributed to this component with a correlation coefficient of r ⬍ ⫺0.9. PC2 was viewed as an “obesity” variable because BMI and body fat contributed to this
component with r ⬎ 0.9. PC3 was influenced by age to a lesser
extent (r ⫽ 0.69).
The lobe region-of-interest analyses revealed that PC2 was significantly associated with ATT, while PC1 and PC3 were not. The
obesity component (PC2) was inversely related to ATT—that is,
higher body fat was associated with shorter ATT in the occipital
lobe (t ⫽ ⫺3.2, P ⫽ .004, Bonferroni-corrected P ⫽ .015), but
none of the other lobes were significant (Bonferroni-corrected
P ⬎ .10). Voxelwise ATT analysis revealed significant findings for
the obesity component in the posterior cingulate, precuneus, lateral occipital cortices, right middle temporal gyrus, and the left
frontal pole (pcorrected ⬍ .05, Fig 2 and Table 3). The insula region
of interest had a LATT that was inversely associated with the pressor component (ie, LATT versus PC1, t ⫽ ⫺2.2, P ⫽ .034), but not
the obesity component, PC2, or PC3 (P ⬎ .27). The inverse association here suggests that higher pulse pressure and SBP were
associated with prolonged ATT. Use of the more parsimonious
ASL model (ie, with only a tissue compartment) did not influence
the voxelwise associations between the vascular risk model and
ATT.
Cognition versus Regional Hemodynamics
The first 3 cognitive PCs (cog-PCs) explained ⬎80% of the cognitive data, and they were subsequently used in the cognitive
model. Cog-PC1, cog-PC2, and cog-PC3 explained 53%, 21%,
0.53
0.21
0.11
and 11% of the variance, respectively (Table 2). Cog-PC1 can be
Note:—TMT indicates Trail-Making Test; PP, pulse pressure.
viewed as an indicator of cognitive dysfunction because the CVLT-LDFR and
digit symbol-coding scores were negative
associations, while the timed Stroop and
Trail-Making Test B measures were positive associations. Each of the 4 cognitive
measures contributed significantly to PC1
(r ⬎ .82 or r ⬍ ⫺0.70). Less relevant cogPCs were cog-PC2, which was influenced
by aging, and cog-PC3, which did not
contribute a meaningful cognitive effect.
Region-of-interest analyses revealed
that cog-PC1 showed negative associations with ATT, indicating that individuFIG 2. Voxelwise results from the regression analyses in which the vascular model (“pressor” and als with poorer cognitive scores had
“obesity” factor) was used as an independent factor that influences ATT. The obesity factor shorter ATT, but these associations did
produced a negative association with ATT, in the sense that the higher the BMI and/or body fat,
the shorter the ATT. Significant voxels in red and yellow are corrected for multiple comparison not survive false discovery rate correction
(pcorrected ⫽ .05).
for multiple comparison (ie, 4 brain
4
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lobes, adjusted P ⬎ .054). Voxelwise analysis was significant,
however, with an association between cognitive decline and
shorter ATT in the precuneus region (pcorrected ⬍ .05, Fig 3 and
Table 3). The cog-PC2 and cog-PC3 did not show any significant associations. The insula LATT region of interest was not
significant with any of the cognitive model parameters (P ⬎
.086). The use of the more parsimonious ASL model did not
influence the voxelwise associations between the cognitive
model and ATT.
nent (PC2) in region-of-interest analyses, albeit with slightly reduced statistical significance (data not shown).
DISCUSSION
The current study demonstrates relationships among cerebral hemodynamics, obesity, and cognition. In older men with CAD,
microvascular ATT decreased in association with obesity and
poorer cognitive function. The implicated regions included areas
of the lateral occipital, precuneus, angular gyrus, middle temporal, and frontal pole. In addition, the macrovascular LATT was
Post Hoc Tests
significantly associated with hypertension, but not with obesity.
Brain regions that were associated with the obesity component of
ATT differences associated with obesity occurred primarily in
the vascular model were used as a mask, and the average ATT in
the parietal and occipital lobes. This result is notable for a few
these regions was calculated for each participant. ATT in this
reasons. First, the regions identified are consistent with the default
obesity mask was significantly correlated with cognitive function
mode network, which is a network implicated in cognitive func(r2 ⫽ 0.21, t ⫽ ⫺2.65, P ⫽ .013).
tion in later life.33,34 Second, precuneus ATT was also found to be
CBF data produced no significant voxels related to componegatively associated with cognitive function. Third, ATT in brain
nents of vascular risk or cognition (pcorrected ⬎ .05) by using
regions identified by obesity explained 21% of the variance in
the same voxelwise analyses as used in the ATT data. However,
cognitive function across the group.
within the ATT mask circumscribed by the vascular risk model
The pressor component was defined by SBP and pulse presresults (PC2, Fig 2), CBF was negatively associated with obesity
sure; however, it did not significantly correlate with regional ATT
(t ⫽ ⫺2.43, P ⫽ .022), and within the ATT mask circumscribed
at a voxelwise level or in the lobe-based region-of-interest analyby the cognitive model results (PC1, Fig 3), CBF was negatively
ses. This finding was unexpected, given previous findings relating
associated with cognitive PC1 (t ⫽ ⫺2.53, P ⫽ .018).
blood pressure with cerebral hemodynamics35; however, the litThe widely used BMI classifications (ie, healthy, overweight,
erature, to date, has focused primarily on large-artery hemodyobese)4 produced results similar to those of the obesity componamics, such as the middle cerebral artery pulsatility index or
blood flow velocity. On the other hand, LATT provides macrovascular information and was significantly associated with the
Table 3: Summary of voxelwise findings
pressor component in the insula region of interest. These results
MNI Coordinates
suggest that vascular risk factors may have differential effects on
No. of Voxels X
Y
Z
cerebral hemodynamics, which appear to be region-specific.
Vascular model
While the ATT results were significant at a voxelwise level after
1) Lateral occipital, R
540
⫺33 ⫺78 30
a conservative correction for multiple comparisons, CBF analyses
2) Lateral occipital, L
38
39 ⫺69 27
3) Middle temporal gyrus, R
36
54 ⫺57
6
were not significant in stringent voxelwise analyses. However,
4) Precuneus
13
0 ⫺60 60
post hoc tests suggested that CBF was decreased in proportion to
5) Frontal pole, L
11
⫺36
45
18
both obesity and cognitive dysfunction in regions identified by
6) Angular gyrus, L
11
⫺39
⫺51 42
ATT; this finding suggests complementary utility between ATT and
7) Lateral occipital, L
4
⫺27 ⫺63 48
CBF measures. The focus of the current study was on estimating
Cognitive model
1) Precuneus
189
0 ⫺66 42
hemodynamic ATT, which requires sampling multiple-postlabel deNote:—MNI indicates Montreal Neurological Institute; R, right; L, left.
lay acquisitions at the potential expense of
the CBF estimate. Our choice of ASL parameters may therefore have contributed to
the reduced sensitivity of CBF analyses.
Ischemic diseases, such as stroke and
steno-occlusive large-artery disease, are
known to produce prolonged ATT due to
the necessity for collateral delivery of
blood to tissue or narrowing of the large
supplying arteries.15,16 In the current
study, prolonged LATT and reduced ATT
were associated with vascular risk factors.
The reduced ATT may be attributed to
altered pulsatile pressures, as reflected in
35,36
and may be an indicaFIG 3. Voxelwise results from the regression analyses, in which the cognitive model was used as the literature,
an independent factor that influences ATT. The first cognitive factor (ie, characterized by poor tion of vessel wall hardening and thickencognitive performance) was positively associated with ATT, in the sense that a lower cognitive
score was associated with a shorter ATT. Significant voxels in blue and cyan are corrected for ing, which are arteriosclerosis processes
consistent with higher BMI and body fat.9
multiple comparison (pcorrected ⫽ .05).
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This study was designed to detect only within-group differences, lacking comparisons between patients with CAD and agematched controls without a history of CAD. Such normative data
would be beneficial to determine whether similar associations are
found in healthy age-related processes. The current study was
limited to men, due to their relative over-representation in cardiac rehabilitation and the need to otherwise control for robust
differences in ATT between men and women.25 Future work
should assess the extent to which both female and male patients
with CAD show similar ATT changes in association with vascular
and cognitive variables. The absence of uncontrolled diabetic
symptoms and current smoking habits within the present cohort
prevented these risk factors from being addressed. Furthermore,
blood lipid levels were not available for all participants. These
additional measures may have helped to explain a greater proportion of the variance and should be explored through future studies. Finally, principal component analysis was used to restrict the
number of independent variables, which amounted to 2 vascular
risk factors and 1 cognitive factor. This strategy proved useful in
identifying voxelwise ATT associations with obesity and cognitive
performance. Correlations with additional risk factors (eg, blood
lipid levels and smoking habits) and, in particular, a mechanistic
understanding of these relationships, including causality, should
be addressed by studies that incorporate a longitudinal design.
CONCLUSIONS
We found that men with CAD have altered cerebral hemodynamics in proportion to both cardiometabolic risk factors and cognitive function. ATT was used as a microvascular measure, while
LATT characterized macrovascular hemodynamics. We observed
that ATT in nodes of the default mode network was associated
with obesity and cognition, whereas LATT was associated with
hypertension. The use of these ASL-derived hemodynamic measures produced novel associations that converge with other
studies to implicate changes in the default mode network as
markers of cognitive decline.34,37 Future studies might explore
mechanisms potentially relating adiposity to cerebral hemodynamics and cognition, such as blood lipid levels associated with
cognitive decline.38
ACKNOWLEDGMENTS
We thank Dr Michael Chappell for useful discussion concerning
the ASL model.
Disclosures: Nathan Herrmann—RELATED: Grant: The Drummond Foundation,*
Physicians’ Services Incorporated Foundation,* Canadian Institutes of Health Research,* Comments: peer-reviewed grants; UNRELATED: Consultancy: Sonexa Therapeutics, Wyeth Pharmaceuticals, Pivina Consulting, Sanofi-Aventis Canada; Grants/
Grants Pending: Alzheimer Society of Canada,* Canadian Institute of Health
Research,* Heart and Stroke Foundation,* Ontario Ministry of Health and Long-Term
Care, Provincial Innovation Fund,* Ontario Brain Institute,* Comments: peer-reviewed grants; Other: Pfizer Canada,* F. Hoffman-La Roche Ltd,* Elan Pharma International Ltd,* Lundbeck Canada,* Comments: research contracts. Krista L. Lanctôt—
RELATED: Grant: Canadian Institutes of Health Research,* The Drummond
Foundation,* Physicians’ Services Incorporated Foundation*; UNRELATED: Consultancy: AbbVie, F. Hoffman-La Roche Ltd; Grants/Grants Pending: Alzheimer’s Society Canada),* Alzheimer’s Drug Discovery Foundation,* Canadian Institute of Health
Research,* Heart and Stroke Foundation,* Consortium of Canadian Centres for Clinical Cognitive Research,* Canadian Consortium on Neurodegeneration in Aging;*
Other: AbbVie,* F. Hoffman-La Roche Ltd,* Elan Pharma International Ltd,* Lundbeck
Canada Inc,* Comments: research funding. *Money paid to the institution.
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REFERENCES
1. Kurth F, Levitt JG, Phillips OR, et al. Relationships between gray
matter, body mass index, and waist circumference in healthy
adults. Hum Brain Mapp 2013;34:1737– 46
2. Raschpichler M, Straatman K, Schroeter ML, et al. Abdominal fat
distribution and its relationship to brain changes: the differential
effects of age on cerebellar structure and function—a cross-sectional, exploratory study. BMJ Open 2013;3:pii: e001915
3. Hassing LB, Dahl AK, Pedersen NL, et al. Overweight in midlife is
related to lower cognitive function 30 years later: a prospective
study with longitudinal assessments. Dement Geriatr Cogn Disord
2010;29:543–52
4. Xu WL, Atti AR, Gatz M, et al. Midlife overweight and obesity increase late-life dementia risk: a population-based twin study. Neurology 2011;76:1568 –74
5. Gianaros PJ, Greer PJ, Ryan CM, et al. Higher blood pressure predicts lower regional grey matter volume: consequences on shortterm information processing. Neuroimage 2006;31:754 – 65
6. Kuo HK, Sorond F, Iloputaife I, et al. Effect of blood pressure on
cognitive functions in elderly persons. J Gerontol A Biol Sci Med Sci
2004;59:1191–94
7. de la Torre JC. Alzheimer disease as a vascular disorder: nosological
evidence. Stroke 2002;33:1152– 62
8. Sato N, Morishita R. Roles of vascular and metabolic components in
cognitive dysfunction of Alzheimer disease: short- and long-term
modification by non-genetic risk factors. Front Aging Neurosci 2013;
5:64
9. Joutel A, Monet-Lepretre M, Gosele C, et al. Cerebrovascular dysfunction and microcirculation rarefaction precede white matter lesions in a mouse genetic model of cerebral ischemic small vessel
disease. J Clin Invest 2010;120:433– 45
10. Asllani I, Borogovac A, Brown TR. Regression algorithm correcting
for partial volume effects in arterial spin labeling MRI. Magn Reson
Med 2008;60:1362–71
11. Groves AR, Chappell MA, Woolrich MW. Combined spatial and
non-spatial prior for inference on MRI time-series. Neuroimage
2009;45:795– 809
12. Chappell MA, MacIntosh BJ, Donahue MJ, et al. Separation of macrovascular signal in multi-inversion time arterial spin labelling
MRI. Magn Reson Med 2010;63:1357– 65
13. Francis ST, Bowtell R, Gowland PA. Modeling and optimization of
look-locker spin labeling for measuring perfusion and transit time
changes in activation studies taking into account arterial blood volume. Magn Reson Med 2008;59:316 –25
14. MacIntosh BJ, Lindsay AC, Kylintireas I, et al. Multiple inflow pulsed
arterial spin-labeling reveals delays in the arterial arrival time in
minor stroke and transient ischemic attack. AJNR Am J Neuroradiol
2010;31:1892–94
15. Macintosh BJ, Marquardt L, Schulz UG, et al. Hemodynamic alterations in vertebrobasilar large artery disease assessed by arterial
spin-labeling MR imaging. AJNR Am J Neuroradiol 2012;33:
1939 – 44
16. Bokkers RP, van Laar PJ, van de Ven KC, et al. Arterial spin-labeling
MR imaging measurements of timing parameters in patients with a
carotid artery occlusion. AJNR Am J Neuroradiol 2008;29:1698 –703
17. Paling D, Thade Petersen E, Tozer DJ, et al. Cerebral arterial bolus
arrival time is prolonged in multiple sclerosis and associated with
disability. J Cereb Blood Flow Metab 2014;34:34 – 42
18. Mak HK, Chan Q, Zhang Z, et al. Quantitative assessment of cerebral hemodynamic parameters by QUASAR arterial spin labeling in
Alzheimer’s disease and cognitively normal elderly adults at 3Tesla. J Alzheimers Dis 2012;31:33– 44
19. Yoshiura T, Hiwatashi A, Yamashita K, et al. Simultaneous measurement of arterial transit time, arterial blood volume, and cerebral
blood flow using arterial spin-labeling in patients with Alzheimer
disease. AJNR Am J Neuroradiol 2009;30:1388 –93
20. MacIntosh BJ, Swardfager W, Crane DE, et al. Cardiopulmonary
fitness correlates with regional cerebral grey matter perfusion and
21.
22.
23.
24.
25.
26.
27.
28.
29.
density in men with coronary artery disease. PLoS One 2014;9:
e91251
Volonghi I, Pendlebury ST, Welch SJ, et al. Cognitive outcomes after
acute coronary syndrome: a population based comparison with
transient ischaemic attack and minor stroke. Heart 2013;99:
1509 –14
Zheng L, Mack WJ, Chui HC, et al. Coronary artery disease is associated with cognitive decline independent of changes on magnetic
resonance imaging in cognitively normal elderly adults. J Am Geriatr Soc 2012;60:499 –504
Kullmann S, Heni M, Veit R, et al. The obese brain: association of
body mass index and insulin sensitivity with resting state network
functional connectivity. Hum Brain Mapp 2012;33:1052– 61
Swardfager W, Herrmann N, Dowlati Y, et al. Relationship between
cardiopulmonary fitness and depressive symptoms in cardiac rehabilitation patients with coronary artery disease. J Rehabil Med
2008;40:213–18
MacIntosh BJ, Filippini N, Chappell MA, et al. Assessment of arterial
arrival times derived from multiple inversion time pulsed arterial
spin labeling MRI. Magn Reson Med 2010;63:641– 47
Gibson E, Gao F, Black SE, et al. Automatic segmentation of white
matter hyperintensities in the elderly using FLAIR images at 3T. J
Magn Reson Imaging 2010;31:1311–22
Chaudhary S, Scouten A, Schwindt G, et al. Hemodynamic effects of
cholinesterase inhibition in mild Alzheimer’s disease. J Magn Reson
Imaging 2013;38:26 –35
Alsop DC, Detre JA, Golay X, et al. Recommended implementation
of arterial spin-labeled perfusion MRI for clinical applications: a
consensus of the ISMRM perfusion study group and the European
consortium for ASL in dementia. Magn Reson Med 2014 Apr 8.
[Epub ahead of print]
Buxton RB, Frank LR, Wong EC, et al. A general kinetic model for
30.
31.
32.
33.
34.
35.
36.
37.
38.
quantitative perfusion imaging with arterial spin labeling. Magn
Reson Med 1998;40:383–96
Hachinski V, Iadecola C, Petersen RC, et al. National Institute of
Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke
2006;37:2220 – 41
Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp
2002;15:1–25
Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44:83–98
Greicius MD, Srivastava G, Reiss AL, et al. Default-mode network
activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004;101:
4637– 42
Celone KA, Calhoun VD, Dickerson BC, et al. Alterations in memory
networks in mild cognitive impairment and Alzheimer’s disease: an
independent component analysis. J Neurosci 2006;26:10222–31
Webb AJ, Simoni M, Mazzucco S, et al. Increased cerebral arterial
pulsatility in patients with leukoaraiosis: arterial stiffness enhances
transmission of aortic pulsatility. Stroke 2012;43:2631–36
Hirata K, Yaginuma T, O’Rourke MF, et al. Age-related changes in
carotid artery flow and pressure pulses: possible implications for
cerebral microvascular disease. Stroke 2006;37:2552–56
Damoiseaux JS, Beckmann CF, Arigita EJ, et al. Reduced restingstate brain activity in the “default network” in normal aging. Cereb
Cortex 2008;18:1856 – 64
Saleem M, Ratnam Bandaru VV, Herrmann N, et al. Ceramides predict verbal memory performance in coronary artery disease patients undertaking exercise: a prospective cohort pilot study. BMC
Geriatr 2013;13:135
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